Synthetic validation data set for use with abn library examples
10000 observations simulated from a DAG with 10 variables from Poisson, Bernoulli and Gaussian distributions.
ex1.dag.data
A data frame, binary variables are factors.The relevant formulas are given below (note these do not give parameter estimates just the form of the relationships, like in glm(), e.g. logit()=1+p1 means a logit link function and comprises of an intercept term and a term involving p1).
binary, logit()=1
poisson, log()=1
gaussian, identity()=1
binary, logit()=1
poisson, log()=1+b1+p1
binary, logit()=1+b1+g1+b2
gaussian, identify()=1+p1+g1+b2
binary, logit()=1+g1+p2
binary, logit()=1+g1+g2
gaussian, identity()=1+g1+b2
## The data is one realisation from the the underlying DAG:
ex1.true.dag <- matrix(data=c(
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,
1,1,0,0,0,0,0,0,0,0,
1,0,1,1,0,0,0,0,0,0,
0,1,1,1,0,0,0,0,0,0,
0,0,1,0,1,0,0,0,0,0,
0,0,1,0,0,0,1,0,0,0,
0,0,1,1,0,0,0,0,0,0), ncol=10, byrow=TRUE)
colnames(ex1.true.dag) <- rownames(ex1.true.dag) <-
c("b1","p1","g1","b2","p2","b3","g2","b4","b5","g3")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.